Mortality of captive house sparrows

Chapter II. T HESIS OBJECTIVES

2.4. Mortality of captive house sparrows

The last study is a follow-up of an unforeseen result of Chapter V, in which I observed an unexpectedly high mortality among the house sparrows while studying their problem-solving performance. In Chapter VI, I investigated the possible causes of the mortality, and motivated by my findings I carried out a systematic review of the literature on captive house sparrows to assess the mortality associated with the various housing conditions required for individual behavioural assays.

C

^HAPTER

III. I

NNOVATIVENESS AND REPRODUCTIVE SUCCESS

Abstract

Success in problem-solving, a form of innovativeness, can help animals exploit their environments, and recent research suggests that it may correlate with reproductive success.

Innovativeness has been proposed to be especially beneficial in urbanized habitats, as suggested by superior problem-solving performance of urban individuals in some species. If there is stronger selection for innovativeness in cities than in natural habitats, we expect problem-solving performance to have a greater positive effect on fitness in more urbanized habitats. We tested this idea in great tits breeding at two urban sites and two forests by measuring their problem-solving performance in an obstacle-removal task and a food-acquisition task. Urban pairs were significantly faster problem-solvers in both tasks. Solving speed in the obstacle-removal task was positively correlated with hatching success and the number of fledglings, whereas performance in the food-acquisition task did not correlate with reproductive success.

These relationships did not differ between urban and forest habitats. Neophobia, sensitivity to human disturbance, and risk taking in the presence of a predator did not explain the relationships of problem-solving performance either with habitat type or with reproductive success. Our results suggest that the benefit of innovativeness in terms of reproductive success is similar in urban and natural habitats, implying that problem-solving skills may be enhanced in urban populations by some other benefits (e.g. increased survival) or reduced costs (e.g. more opportunities to gain practice with challenging tasks).

This chapter is a modified version of the research article “Preiszner, B., Papp, S., Pipoly, I., Seress, G., Vincze, E., Liker, A. & Bókony, V. (2017) Problem-solving performance and reproductive success of great tits in urban and forest habitats. Animal Cognition 20:53-63.”

3.1. Introduction

Accumulating evidence suggest that innovative behaviour can have positive fitness consequences (Keagy et al. 2009; Mateos-Gonzalez et al. 2011; Cauchard et al. 2013), but these benefits may vary between habitat types, and selection may favour an innovative phenotype more strongly in more challenging environments. For example in chickadees (Poecile spp.) individuals living in harsher environments have enhanced spatial memory and better problem-solving performance compared to conspecifics living under milder conditions; this difference has been attributed to the importance of food caching, and the cognitive skills required for it, which is necessary for survival in harsh habitats (reviewed in Pravosudov and Roth 2013).

Along a similar logic, innovativeness may be particularly important in urban environments, because urban animals are exposed to several kinds of novel or variable stimuli such as fragmented landscapes, noise and light pollution, disturbance by domestic animals and humans, and new food resources such as garbage (Sol et al. 2013). Accordingly, individuals from more urbanized habitats were found to be more successful in certain problem-solving tasks in three avian species (Liker and Bókony 2009; Sol et al. 2011; Audet et al. 2016), although the relationship between urbanization and innovativeness is equivocal (Papp et al. 2015; Audet et al. 2016). Consequently, if innovativeness is particularly relevant in urban habitats, we may expect that it has a stronger effect on fitness than in non-urbanized habitats.

We tested this idea in the great tit, which is one of the most common breeding birds in both urban areas and natural forests in Europe (Burfield and van Bommel 2004). We measured innovativeness in urbanized and forest-dwelling breeding pairs in two different problem-solving situations, an obstacle-removal task and a food-acquisition task, and monitored their breeding success. We investigated whether 1) urban pairs outperform their forest-dwelling conspecifics in the speed of problem-solving, 2) individuals with superior problem-solving performance have higher breeding success within their habitats, and 3) the relationship between problem-solving performance and breeding success is more pronounced in urban habitats than in forests. Furthermore, we examined whether any of the above relationships is mediated or confounded by differences in three behavioural traits that have been found to be related to problem-solving performance as well as to urbanization in several species: neophobia (Sol et al. 2011; Miranda et al. 2013; Cauchard et al. 2013), sensitivity to predation risk (Seress et al.

2011; Cole et al. 2012) and sensitivity to human disturbance (Cole et al. 2012; Vincze et al.

2016).

3.2. Methods

We tested 55 wild great tit pairs nesting in artificial nest boxes in 2 urban and 2 forest habitats in 2013. The urban study sites are located in Veszprém (47°05’17”N, 17°54’29”E) and Balatonfüred (46°57’30”N, 17°53’34”E), whereas the forest study sites are a downy oak (Quercus pubescens) and south European flowering ash (Fraxinus ornus) forest at Vilma-puszta (47°05’02”N, 17°52’01”E) and a beech (Fagus sylvatica) and hornbeam (Carpinus betulus) forest near Szentgál (47°06’39”N, 17°41’17”E) in Hungary.

Throughout the breeding season we checked the nest boxes twice a week and recorded the number of eggs and/or chicks at each visit. The experimental protocol began by catching one of the parents (excepting a few pairs where one or both parents had already been ringed) using a nest box trap when the chicks were 5-9 (mean ± SE = 6.18 ± 0.16) days old, considering the day of hatching of the first chick as day 1. Upon capture we ringed the birds with a unique combination of a metal ring and 3 plastic colour rings, and we recorded their age class (2^nd calendar year or older) and sex, both based on plumage characteristics (Svensson 1992).

Ringing one of the parents before the behavioural tests ensured that the sex of the parents could be recognized unambiguously during all observations, as it was not always possible to sex the birds by plumage from the videos (see below). We trapped only one parent before the tests to minimize stress and the risk of nest desertion. Between days 6-16 of chick age we conducted five behavioural tests at each nest as detailed below; then we trapped and ringed the other parent (if it had not been ringed earlier) following the last test, so that individuals could be identified in later breeding episodes. Because trapping might have affected the birds’ behaviour (Schlicht and Kempenaers 2015), the trapping status of each individual (i.e. trapped a few days before the tests or not) and each pair (i.e. one or no parent trapped a few days before the tests) was taken into account in the analyses (see below). At the age of 13-17 (mean ± SE = 15.07 ± 0.12) days, we ringed the chicks and measured their body mass and tarsus length.

3.2.1. Behavioural tests

First we assayed the parents’ neophobia between days 6-10 (mean ± SE = 7.98 ± 0.16) of chick age. After 30 minutes of baseline observation we fixed a small rubber ball with adhesive putty on the platform next to the entrance of the nest box (Figure III.1, panel C), and observed the nest box until both parents entered the nest, or for 30 minutes. We assessed the neophobia of

each parent by measuring the latency to enter the nest box after the observer had placed the ball and left the vicinity of the nest.

The next two tests were designed to assay problem-solving performance. First, all pairs were tested in an obstacle-removal task between 7-11 (mean ± SE = 9.15 ± 0.15; mean difference between forest and urban pairs: 0.19 ± 0.31) days of chick age. Before the test, during a 30 minutes period of baseline observation, there was a ca. 3×7 cm grey feather fixed with adhesive putty on the platform near the entrance. The birds had been familiarized with this situation because we had put a similar feather near the entrance upon the start of egg laying, and replaced it with another feather at every nest check (whether or not it was removed by the birds between the successive nest checks) until the obstacle-removal test. In most cases these feathers had been removed by the birds between the successive nest checks, but we kept no record whether or when it happened. At the start of the test we blocked the entrance by fixing a similar grey feather in front of it using magnetic tape, and observed the nest box until one of the parents removed the feather and entered the nest, or for 30 minutes. To remove the feather, the bird had to grab it with the beak or a foot to pull it off (Figure III.1, panel D).

In the second problem-solving test, the parents were tested in a food-acquisition task between 8-13 (mean ± SE = 10.35 ± 0.19; mean difference between forest and urban pairs: 0.56

± 0.37) days of chick age. During the 30 minutes of baseline observation before the test we provided the birds with 3 mealworms (Tenebrio molitor larvae) in a well on the platform near the entrance of the nest box. This situation was familiar for the birds because we provided 3 mealworms in the same well upon every nest check from the start of egg laying. At the start of the test we topped up the number of mealworms in the well to 3, and we covered the well by a transparent plastic lid that was fixed at its two ends by sticking small pieces of toothpicks into prepared holes. In order to reach the mealworms, the birds had to remove at least one toothpick and move the lid, or lift the lid off from the toothpicks by pulling it upwards (Figure III.1, panel E). We observed the nest box until one of the parents removed the lid and took out at least one mealworm, or for 30 minutes.

Figure III.1: Methods for observing problem-solvingperformance of breeding great tits.

A) Female at the nest box with a permanent hide for video camera.

B) Familiarizing the birds with the test equipment upon each nest check: feather fixed on the platform and mealworms placed in the well.

C) Rubber ball temporarily attached on the platform during the neophobia test.

D) Entrance blocked by a feather during the obstacle-removal task. For a video sample showing a solving bird, see: http://www.edge-cdn.net/video_1062366?playerskin=37016

E) Mealworms covered by a lid fixed with sticks during the food-acquisition task. For a video sample showing a solving bird, see: http://www.edge-cdn.net/video_1062368?playerskin=37016

After the first 3 tests, when the chicks were 9-16 (mean ± SE = 12.81 ± 0.14) days old, each pair was observed in two more behavioural assays, the order of which was randomly chosen at each nest. These two tests were designed to assess the birds’ sensitivity to predation risk and to human disturbance. At the beginning of the predation-risk test we placed a ca. 1 m high tripod on the ground, setting up the top end 3 m from the nest box entrance. The observation started when the experimenter left the vicinity of the nest. After 15 minutes of baseline observation we fixed a taxidermally mounted Eurasian collared dove (Streptopelia decaocto) on the tripod for 10 minutes, then removed the dove and conducted an additional 10 minutes observation. After this, a taxidermally mounted Eurasian sparrowhawk (Accipiter nisus) was fixed on the tripod for 10 minutes, and after the removal of the sparrowhawk the observations were carried out for a further 10 minutes. Thus the entire test was 55 minutes long.

We measured the number of visits (i.e. entering the nest box) per minute (henceforth visit rate) by both parents in each 10-minutes interval; then we quantified their response to predation risk as visit rate recorded in the 10 minutes after the removal of the sparrowhawk minus the visit rate recorded in the 10 minutes after the removal of the dove.

The human-disturbance test followed a similar design as the predation-risk test, but no tripod was placed near the nest. Again, the observation started when the experimenter left the vicinity of the nest. After the first 15-minutes baseline observation, a person stood under the nest box for 10 minutes. After the person had left, we observed the nest for a further 10 minutes, and thus the entire test was 35 minutes long. We measured the number of visits per minute by both parents in each interval; then we quantified their response to human disturbance as visit rate in the 10 minutes after the person had left minus the visit rate in the 15 minutes before the arrival of the person to the nest box.

Each test was conducted on a different day. All observations were made using a small (98 × 58 × 34 mm) camera hidden in a plastic box that was permanently attached to the nest box ca. 15 cm from the entrance (Figure III.1, panel A). All tests began with a few-minutes period that supposedly attracted the attention of the parent birds (i.e. the experimenter walked into their territory and installed the camera on the nest box and the other devices needed for the test); since the parents could hide in the canopy when approaching the nest boxes, it was not possible to ascertain when they became aware of the stimuli. The 5 behavioural tests were repeated with the same protocol in later breeding episodes in the same breeding season (2013) for a subset of the same pairs in order to test individual consistency in problem-solving performance.

29 3.2.2. Statistical analyses

In the problem-solving tests, we measured solving latency as the time the solving parent took to remove the feather in the obstacle-removal task or open the well in the food-acquisition task after it first landed at the entrance. We used the criterion of landing at the entrance because proximity to the task was necessary for starting to attempt to problem solve. Hence, time spent potentially visually inspecting the task at close range was included in the problem-solving latency. In contrast, during neophobia tests, birds typically entered the nest box very soon after landing. In this case, although birds occasionally spent considerable time inspecting the novel object form a greater distance, this time could not be quantified given our reliance on close-up video. If a bird landed on the nest box at least once during the 30 minutes of the test but did not solve, we considered it as a non-solver. Birds that did not visit the nest box during the 30 minutes of the test were treated as non-participating. Individuals whose mate solved the given task were treated as non-measured because their performance could not be quantified (i.e. the test ended when one of the parents solved, so it is unknown if the other parent would have been a solver, a solver, or participating if it had had 30 minutes). Non-measured and non-participating birds were excluded from all individual-level analyses (Table III.1). Non-solvers were assigned the maximal latency (1860 sec, i.e. the duration of the test plus 1 minute). To analyse whether problem-solving latency is individually consistent, we used the data of 26 pairs that were tested in 2 consecutive breeding episodes within the season. Within each task, we correlated the latencies between their first and second tests if the solver individual was the same in both tests or if none of the parents solved in one or both tests. In all other analyses (detailed below), we used only the data collected during the rearing of the first brood of the year for each pair.

To analyse the effects of habitat type and potential confounder variables on problem-solving performance, we used Cox’s proportional hazards models with problem-solving latency of the respective task as dependent variable, treating maximal latencies as censored observations.

Initial models contained habitat type (urban vs. forest), provisioning rate (the number of visits of the parents during the baseline observation of the respective test divided by the number of nestlings alive on the day of the respective test), date of the test (number of days since 1st of May), age of nestlings on the day of the test, time of day at the start of the test (number of minutes since 7:00), and the parents’ age class and trapping status (as explained below).

To measure breeding success, we calculated the following variables: clutch size (i.e. the maximum number of eggs observed in the nest); hatching success (i.e. the proportion of eggs that hatched); number of fledglings (i.e. number of nestlings alive at the age of ringing);

proportion of chicks fledged (i.e. the proportion of hatchlings that survived to ringing age);

mean tarsus length (± 0.1 mm) and mean body mass (± 0.1 g) of fledglings measured at ringing.

Linear mixed-effects models were used with study site as random factor to investigate whether each measure of breeding success is predicted by solving latency in the two problem-solving tasks. In the models of hatching success and proportion of chicks fledged, we used quasi-binomial error distribution with logit link function. Solving latency of the respective test, habitat type, hatching date of the first chick, parents’ age class and trapping status (see below) were included in the initial models as predictors, along with the solving latency × habitat type interaction to test whether the effect of solving latency differs between urban and forest pairs.

In all analyses, we also tested the effects of neophobia, response to predation risk, response to human disturbance, and their interactions with habitat type by adding each to the initial models separately (we did not include all potential confounders into one model to avoid over-parametrization). Each initial model was then reduced by omitting the term associated with the largest p-value stepwise, except that we always retained the predictor that we were primarily interested in, i.e. habitat type in the Cox’s analyses and problem-solving latency in the mixed models, regardless of their significance level, to estimate their effects even if they were not significant. Also, we always kept habitat type in the mixed models to control for the difference between urban and forest breeding parameters (Solonen 2001; Bailly et al. 2015).

Other predictors and interactions were omitted if they had p > 0.05.

We used two approaches throughout the analyses: in one set we used pairs as the units of analysis while in the other set we used the data of individuals. This dual approach was necessary because we had only one solver individual per pair, so the confounding variables can be calculated in two equally relevant ways. First, when analysing pairs, we considered that breeding success may depend on the traits of both parents, thus we coded the parents’ age class and trapping status as whether or not the pair contained at least one individual that was older than 2^nd calendar year and had been trapped before the behavioural tests, respectively; and we expressed neophobia, response to predation risk, and response to human disturbance as the average of the two parents’ values. Second, when analysing individuals, we focused on the traits of the solving parent (this could not be done in the analyses of pairs because there was no solving parent in the unsuccessful pairs). Thus, in the analyses of females, we compared the data of solver females to non-solver pairs (i.e. non-solver females) omitting those pairs in which the male was the solver because in these latter cases we could not measure female performance.

Similarly, in the analyses of males, we used the data of solver males and non-solver pairs and omitted the pairs in which the female was the solver (note that this could not be done in the

obstacle-removal task with reasonable power because there were only 5 successful males; thus this task was analysed only by using pair and female performance). In these individual-level analyses we used the solver parent’s age class, trapping status, neophobia, response to predation risk, and response to human disturbance as predictors.

In document A viselkedési flexibilitás kapcsolata a sikerességgel szaporodási és szociális helyzetekben (Pldal 22-0)